ModAlkAnilineSeq: ModAlkAnilineSeq class to analyze AlkAnilineSeq data
In RNAmodR.AlkAnilineSeq: Detection of m7G, m3C and D modification by AlkAnilineSeq

Description Usage Arguments Value Author(s) References Examples

View source: R/Modifier-AlkAnilineSeq-class.R

7-methyl guanosine (m7G), 3-methyl cytidine (m3C) and Dihydrouridine (D) are commonly found in rRNA and tRNA and can be detected classically by primer extension analysis. However, since the modifications do not interfere with Watson-Crick base pairing, a specific chemical treatment is employed to cause strand breaks specifically at the modified positions.

This classical protocol was converted to a high throughput sequencing method call AlkAnilineSeq and allows modified position be detected by an accumulation of 5'-ends at the N+1 position. Since the identify of the unmodified nucleotide is different for the three modified nucleotides, they modification can be detected at the same time from the same samples.

dataType is c("NormEnd3SequenceData","PileupSequenceData"):

The ModAlkAnilineSeq class uses the NormEnd5SequenceData class to store and aggregate data along the transcripts. This includes normalized values against the whole transcript (normalzed cleavage) and normalized values against the overlapping reads (stop ratio), which are used to score for modified positions.

In addition the PileupSequenceData class is used as well, to check, whether the base is can be called according to the expected sequence identity.

Only samples named treated are used for this analysis. Normalization to untreated samples is currently not used.

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ModAlkAnilineSeq(x, annotation = NA, sequences = NA, seqinfo = NA, ...)

ModSetAlkAnilineSeq(x, annotation = NA, sequences = NA, seqinfo = NA, ...)

`x`	the input which can be of the different types depending on whether a `ModRiboMethSeq` or a `ModSetRiboMethSeq` object is to be constructed. For more information have a look at the documentation of the `Modifier` and `ModifierSet` classes.
`annotation`	annotation data, which must match the information contained in the BAM files. This is parameter is only required if `x` if not a `Modifier` object.
`sequences`	sequences matching the target sequences the reads were mapped onto. This must match the information contained in the BAM files. This is parameter is only required if `x` if not a `Modifier` object.
`seqinfo`	An optional `Seqinfo` argument or character vector, which can be coerced to one, to subset the sequences to be analyzed on a per chromosome basis.
`...`	Optional arguments overwriting default values, which are minLength: The minimal read length to be used for the analysis (default: `minLength = 9L`). minSignal: The minimal signal at the position as integer value (default: `minSignal = 10L`). If the reaction is very specific a lower value may need to be used minScoreNC: minimum for score (normalized cleavage) to identify m7G, m3C and D positions de novo (default: `minScoreNC = 50L`) minScoreSR: minimum for score (stop ration) to identify m7G, m3C and D positions de novo (default: `minScoreSR = 0.5`) minScoreBaseScore: minimum score for base calling (0.0-1.0) (default: `minScoreSR = 0.9`) scoreOperator: how the minimal score should be used as logical operator. "&" requires all minimal values to be exceeded, whereas "\|" detects positions, if at least one minimal values is exceeded (default: `scoreOperator = "&"`). other arguments which are passed on to `End5SequenceData`

a ModAlkAnilineSeq or ModSetAlkAnilineSeq object

Felix G.M. Ernst [aut]

- Marchand V, Ayadi L, __Ernst FGM__, Hertler J, Bourguignon-Igel V, Galvanin A, Kotter A, Helm M, __Lafontaine DLJ__, Motorin Y (2018): "AlkAniline-Seq: Profiling of m7 G and m3 C RNA Modifications at Single Nucleotide Resolution." Angewandte Chemie (International ed. in English) 57 (51), P. 16785<e2><80><93>16790. DOI: 10.1002/anie.201810946.

library(RNAmodR.Data)
library(rtracklayer)
annotation <- GFF3File(RNAmodR.Data.example.AAS.gff3())
sequences <- RNAmodR.Data.example.AAS.fasta()
files <- list("wt" = c(treated = RNAmodR.Data.example.wt.1()),
              "Bud23del" = c(treated = RNAmodR.Data.example.bud23.1()),
              "Trm8del" = c(treated = RNAmodR.Data.example.trm8.1()))
# Creating a Modifier object of type ModRiboMethSeq
maas <- ModAlkAnilineSeq(files[[1]], annotation = annotation,
                         sequences = sequences)
# Creating a ModifierSet object of type ModSetRiboMethSeq
msaas <- ModSetAlkAnilineSeq(files, annotation = annotation,
                             sequences = sequences)